Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
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Ten studies that trained on synthetic data never meaningfully tested on real data.
“and perhaps worst, ten studies that trained on synthetic data never even tested (meaningfully) on real data (Hou et al., 2017, 2018; Pei et al., 2017; Xie et al., 2017; Bier et al., 2018; Foote et al., 2019; Guan et al., 2019; Yang and Chen, 2019; Neumann et al., 2020; Zhang et al., 2020).”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Hou et al., 2017
For the cited Hou et al. (2017) study, the Experiments and Results section (pp. 5–6) explicitly reports testing on a real-world set of 34 fetal brain MRI scans and gives a prediction-error result. It also describes an experiment using motion-corrupted fetal MRI slices to test the method under real conditions. The paper therefore contradicts the claim that this study never meaningfully tested on real data; it cannot establish what the other nine cited studies did. (LLM, conf=0.88)
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- Authors
- Hou, B., Alansary, A., McDonagh, S., Davidson, A., Rutherford, M., Hajnal, J. V., Rueckert, D., Glocker, B., & Kainz, B.
- Affiliations
- Imperial College London (GB)
- King's College London (GB)
- Corresponding author
- Hou, Benjamin
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 296-304
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2591825272
- Cited by
- 68
- References
- 15
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 5 · 2023: 5 · 2022: 7 · 2021: 15 · 2020: 8 · 2019: 4 · 2018: 8 · 2017: 4
- FWCI
- 1.92
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia
- Wellcome Trust (102431)
- Engineering and Physical Sciences Research Council (102431, EP/N024494/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- 2D-3D registration, subject motion, intensity-based registration, fetal MRI, motion compensation, CT image reconstruction, X-ray projections
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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